Interactions in statistical models are never especially easy to interpret. Throw in non-normal outcome variables and non-linear prediction functions and they become even more difficult to understand.
In this training, we review how interactions work in linear models. We’ll then step back with an overview of how Poisson and logistic regression equations work, and how their results are more difficult to interpret than linear regression.
Finally, we’ll walk through examples of interactions with categorical and numeric variables in Poisson and logistic regression. Along the way, we’ll provide graphical and numeric interpretations, and talk about why these specific approaches are best for these types of models.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Kim is a Statistically Speaking mentor and workshop instructor for The Analysis Factor. She is also owner/lead consultant at K.R. Love Quantitative Consulting and Collaboration.
She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center.
Kim has more than a decade of professional and academic experience in the fields of regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design. She has a B.A. in mathematics from the University of Virginia, and an M.S. and PhD in statistics from Virginia Tech.
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